# np.kriging: Nonparametric (residual) kriging In npsp: Nonparametric Spatial Statistics

 np.kriging R Documentation

## Nonparametric (residual) kriging

### Description

Compute simple kriging or residual kriging predictions (and also the corresponding simple kriging standard errors ). Currently, only global (residual) simple kriging is implemented.

### Usage

``````np.kriging(object, ...)

## Default S3 method:
np.kriging(
object,
svm,
lp.resid = NULL,
ngrid = object\$grid\$n,
intermediate = FALSE,
...
)

## S3 method for class 'np.geo'
np.kriging(object, ngrid = object\$grid\$n, intermediate = FALSE, ...)

kriging.simple(x, y, newx, svm, intermediate = FALSE)
``````

### Arguments

 `object` object used to select a method: local polynomial estimate of the trend (class `locpol.bin`) or nonparametric geostatistical model (class extending `np.geo`). `...` further arguments passed to or from other methods. `svm` semivariogram model (of class extending `svarmod`). `lp.resid` residuals (defaults to `residuals(object)`). `ngrid` number of grid nodes in each dimension. `intermediate` logical, determines whether the intermediate computations are included in the output (component `kriging`; see Value). These calculations can be reused, e.g. for bootstrap. `x` vector/matrix with data locations (each component/row is an observation location). `y` vector of data (response variable). `newx` vector/matrix with the (irregular) locations to predict (each component/row is a prediction location). or an object extending `grid.par`-`class` (`data.grid`).

### Value

`np.kriging()`, and `kriging.simple()` when `newx` defines gridded data (extends `grid.par` or `data.grid` classes), returns an S3 object of class `krig.grid` (kriging results + grid par.). A `data.grid` object with the additional (some optional) components:

 `kpred` vector or array (dimension `\$grid\$n`) with the kriging predictions. `ksd` vector or array with the kriging standard deviations. `kriging` (if requested) a list with 4 components: `lambda` matrix of kriging weights (columns correspond with predictions and rows with data)). `cov.est` (estimated) covariance matrix of the data. `chol` Cholesky factorization of `cov.est`. `cov.pred` matrix of (estimated) covariances between data (rows) and predictions (columns).

When `newx` is a matrix of coordinates (where each row is a prediction location), `kriging.simple()` returns a list with the previous components (`kpred`, `ksd` and, if requested, `kriging`).

`np.fitgeo`, `locpol`, `np.svar`.

### Examples

``````geomod <- np.fitgeo(aquifer[,1:2], aquifer\$head)
krig.grid <- np.kriging(geomod, ngrid = c(96, 96)) # 9216 locations
old.par <- par(mfrow = c(1,2))
simage(krig.grid, 'kpred', main = 'Kriging predictions',
xlab = "Longitude", ylab = "Latitude", reset = FALSE )
simage(krig.grid, 'ksd', main = 'Kriging sd', xlab = "Longitude",
ylab = "Latitude" , col = hot.colors(256), reset = FALSE)
par(old.par)
``````

npsp documentation built on May 29, 2024, 5:31 a.m.